R Notebook

df <- read.csv("merged-new-version2.csv", header =TRUE, sep=",")
#df <- df[!complete.cases(df), ]  
df

df <- read.csv("merged-variety.csv", header =TRUE, sep=",")
#df <- df[!complete.cases(df), ]  
df

df <- read.csv("merged-added-functions.csv", header =TRUE, sep=",")
#df <- df[!complete.cases(df), ]  
df

df <- read.csv("merged-sim-best.csv", header =TRUE, sep=",")
#df <- df[!complete.cases(df), ]  
df

df$ln_novelty <- log(df$novelty+1)
df$ln_total <- log(df$total+1) 
df$ln_exploration <- log(df$exploration+1) 
df$group = factor(df$group)
df$ln_len_unique <- log(df$len_unique+1) 
df$ln_added_sum <- log(df$added_sum+1)
df$ln_sim_best <- log(df$sim.to.best+1)
df$ln_count <- log(df$count+1)
df

df_new <- df[, sapply(df, is.numeric)]
cor(df_new, use = "complete.obs", method = "spearman" )

                                 X   Unnamed..0        phase     novelty abs_perform_diff_best      Q7_Q7_1
X                      1.000000000  1.000000000  0.242356186 -0.04000008          -0.039079868  0.005237315
Unnamed..0             1.000000000  1.000000000  0.242356186 -0.04000008          -0.039079868  0.005237315
phase                  0.242356186  0.242356186  1.000000000  0.11757506          -0.092602001 -0.008916463
novelty               -0.040000085 -0.040000085  0.117575064  1.00000000          -0.261877799  0.070537895
abs_perform_diff_best -0.039079868 -0.039079868 -0.092602001 -0.26187780           1.000000000  0.075157400
Q7_Q7_1                0.005237315  0.005237315 -0.008916463  0.07053789           0.075157400  1.000000000
Q7_Q7_2               -0.049826494 -0.049826494 -0.007496913  0.17493630          -0.143353719  0.599305380
Q8_Q8_1               -0.040010136 -0.040010136 -0.008881488  0.15957243          -0.132036172  0.228181513
Q10                    0.072013496  0.072013496 -0.009800966  0.09059810          -0.239114006  0.169440714
count                 -0.048611362 -0.048611362 -0.137451974  0.31528952          -0.399745885 -0.041133553
total                 -0.090563629 -0.090563629  0.204885844  0.35310977          -0.727372643 -0.092576997
user.requirement      -0.097313308 -0.097313308  0.168324538  0.25497735          -0.586581246 -0.120249752
infovis               -0.071285195 -0.071285195  0.199121719  0.24719465          -0.617800457 -0.045449265
novelty_score          0.019692993  0.019692993  0.163234040  0.25847647          -0.603222132 -0.109564379
exploration           -0.136761136 -0.136761136 -0.242448405  0.29353889          -0.165601842 -0.047418598
Group                 -0.968129145 -0.968129145  0.000000000  0.13234300           0.002406986 -0.003551689
len_unique            -0.083893425 -0.083893425  0.188120655  0.53772703          -0.534026862  0.057679060
added_sum             -0.106697125 -0.106697125 -0.143686890  0.36485491          -0.284894816 -0.013155276
sim.to.best           -0.081539257 -0.081539257 -0.247582068  0.08055287          -0.332089485 -0.077542723
ln_novelty            -0.040000085 -0.040000085  0.117575064  1.00000000          -0.261877799  0.070537895
ln_total              -0.090563629 -0.090563629  0.204885844  0.35310977          -0.727372643 -0.092576997
ln_exploration        -0.136761136 -0.136761136 -0.242448405  0.29353889          -0.165601842 -0.047418598
ln_len_unique         -0.083893425 -0.083893425  0.188120655  0.53772703          -0.534026862  0.057679060
ln_added_sum          -0.106697125 -0.106697125 -0.143686890  0.36485491          -0.284894816 -0.013155276
ln_sim_best           -0.081539257 -0.081539257 -0.247582068  0.08055287          -0.332089485 -0.077542723
                            Q7_Q7_2      Q8_Q8_1          Q10       count       total user.requirement
X                     -0.0498264942 -0.040010136  0.072013496 -0.04861136 -0.09056363      -0.09731331
Unnamed..0            -0.0498264942 -0.040010136  0.072013496 -0.04861136 -0.09056363      -0.09731331
phase                 -0.0074969129 -0.008881488 -0.009800966 -0.13745197  0.20488584       0.16832454
novelty                0.1749363000  0.159572432  0.090598097  0.31528952  0.35310977       0.25497735
abs_perform_diff_best -0.1433537188 -0.132036172 -0.239114006 -0.39974589 -0.72737264      -0.58658125
Q7_Q7_1                0.5993053799  0.228181513  0.169440714 -0.04113355 -0.09257700      -0.12024975
Q7_Q7_2                1.0000000000  0.304176579  0.253625610  0.02255924  0.12261038       0.05494034
Q8_Q8_1                0.3041765787  1.000000000  0.299848563  0.03262785  0.13638701       0.11647285
Q10                    0.2536256098  0.299848563  1.000000000  0.11488257  0.21702201       0.17391984
count                  0.0225592434  0.032627854  0.114882572  1.00000000  0.45783201       0.32531875
total                  0.1226103822  0.136387012  0.217022015  0.45783201  1.00000000       0.82479921
user.requirement       0.0549403441  0.116472851  0.173919837  0.32531875  0.82479921       1.00000000
infovis                0.1341896399  0.110140096  0.174082916  0.37053388  0.82990134       0.78240812
novelty_score          0.0933017185  0.116987509  0.169785938  0.37929023  0.83439087       0.52965641
exploration           -0.0258093480 -0.049361187  0.001496788  0.62586084  0.33055456       0.21402281
Group                  0.0602397593  0.048981570 -0.070924503  0.03675437  0.16405944       0.15641934
len_unique             0.1239927882  0.253341795  0.234669806  0.43754571  0.66331157       0.45947912
added_sum              0.0100795268  0.050159869  0.097675586  0.66373813  0.44949580       0.30497294
sim.to.best           -0.0007748152 -0.088902334 -0.008770066  0.25114015  0.30757319       0.22293568
ln_novelty             0.1749363000  0.159572432  0.090598097  0.31528952  0.35310977       0.25497735
ln_total               0.1226103822  0.136387012  0.217022015  0.45783201  1.00000000       0.82479921
ln_exploration        -0.0258093480 -0.049361187  0.001496788  0.62586084  0.33055456       0.21402281
ln_len_unique          0.1239927882  0.253341795  0.234669806  0.43754571  0.66331157       0.45947912
ln_added_sum           0.0100795268  0.050159869  0.097675586  0.66373813  0.44949580       0.30497294
ln_sim_best           -0.0007748152 -0.088902334 -0.008770066  0.25114015  0.30757319       0.22293568
                          infovis novelty_score  exploration        Group  len_unique   added_sum
X                     -0.07128520    0.01969299 -0.136761136 -0.968129145 -0.08389343 -0.10669712
Unnamed..0            -0.07128520    0.01969299 -0.136761136 -0.968129145 -0.08389343 -0.10669712
phase                  0.19912172    0.16323404 -0.242448405  0.000000000  0.18812066 -0.14368689
novelty                0.24719465    0.25847647  0.293538891  0.132342998  0.53772703  0.36485491
abs_perform_diff_best -0.61780046   -0.60322213 -0.165601842  0.002406986 -0.53402686 -0.28489482
Q7_Q7_1               -0.04544927   -0.10956438 -0.047418598 -0.003551689  0.05767906 -0.01315528
Q7_Q7_2                0.13418964    0.09330172 -0.025809348  0.060239759  0.12399279  0.01007953
Q8_Q8_1                0.11014010    0.11698751 -0.049361187  0.048981570  0.25334180  0.05015987
Q10                    0.17408292    0.16978594  0.001496788 -0.070924503  0.23466981  0.09767559
count                  0.37053388    0.37929023  0.625860839  0.036754373  0.43754571  0.66373813
total                  0.82990134    0.83439087  0.330554560  0.164059440  0.66331157  0.44949580
user.requirement       0.78240812    0.52965641  0.214022810  0.156419343  0.45947912  0.30497294
infovis                1.00000000    0.55558433  0.225677837  0.136721048  0.52314195  0.31672078
novelty_score          0.55558433    1.00000000  0.257635574  0.034698153  0.52536621  0.36970115
exploration            0.22567784    0.25763557  1.000000000  0.100359811  0.32609569  0.89458659
Group                  0.13672105    0.03469815  0.100359811  1.000000000  0.16517282  0.09810871
len_unique             0.52314195    0.52536621  0.326095688  0.165172821  1.00000000  0.53400908
added_sum              0.31672078    0.36970115  0.894586586  0.098108711  0.53400908  1.00000000
sim.to.best            0.28889292    0.19532082  0.291410215  0.030623963  0.22208560  0.26640203
ln_novelty             0.24719465    0.25847647  0.293538891  0.132342998  0.53772703  0.36485491
ln_total               0.82990134    0.83439087  0.330554560  0.164059440  0.66331157  0.44949580
ln_exploration         0.22567784    0.25763557  1.000000000  0.100359811  0.32609569  0.89458659
ln_len_unique          0.52314195    0.52536621  0.326095688  0.165172821  1.00000000  0.53400908
ln_added_sum           0.31672078    0.36970115  0.894586586  0.098108711  0.53400908  1.00000000
ln_sim_best            0.28889292    0.19532082  0.291410215  0.030623963  0.22208560  0.26640203
                        sim.to.best  ln_novelty    ln_total ln_exploration ln_len_unique ln_added_sum
X                     -0.0815392574 -0.04000008 -0.09056363   -0.136761136   -0.08389343  -0.10669712
Unnamed..0            -0.0815392574 -0.04000008 -0.09056363   -0.136761136   -0.08389343  -0.10669712
phase                 -0.2475820684  0.11757506  0.20488584   -0.242448405    0.18812066  -0.14368689
novelty                0.0805528689  1.00000000  0.35310977    0.293538891    0.53772703   0.36485491
abs_perform_diff_best -0.3320894854 -0.26187780 -0.72737264   -0.165601842   -0.53402686  -0.28489482
Q7_Q7_1               -0.0775427227  0.07053789 -0.09257700   -0.047418598    0.05767906  -0.01315528
Q7_Q7_2               -0.0007748152  0.17493630  0.12261038   -0.025809348    0.12399279   0.01007953
Q8_Q8_1               -0.0889023336  0.15957243  0.13638701   -0.049361187    0.25334180   0.05015987
Q10                   -0.0087700658  0.09059810  0.21702201    0.001496788    0.23466981   0.09767559
count                  0.2511401493  0.31528952  0.45783201    0.625860839    0.43754571   0.66373813
total                  0.3075731914  0.35310977  1.00000000    0.330554560    0.66331157   0.44949580
user.requirement       0.2229356796  0.25497735  0.82479921    0.214022810    0.45947912   0.30497294
infovis                0.2888929166  0.24719465  0.82990134    0.225677837    0.52314195   0.31672078
novelty_score          0.1953208213  0.25847647  0.83439087    0.257635574    0.52536621   0.36970115
exploration            0.2914102146  0.29353889  0.33055456    1.000000000    0.32609569   0.89458659
Group                  0.0306239632  0.13234300  0.16405944    0.100359811    0.16517282   0.09810871
len_unique             0.2220856022  0.53772703  0.66331157    0.326095688    1.00000000   0.53400908
added_sum              0.2664020253  0.36485491  0.44949580    0.894586586    0.53400908   1.00000000
sim.to.best            1.0000000000  0.08055287  0.30757319    0.291410215    0.22208560   0.26640203
ln_novelty             0.0805528689  1.00000000  0.35310977    0.293538891    0.53772703   0.36485491
ln_total               0.3075731914  0.35310977  1.00000000    0.330554560    0.66331157   0.44949580
ln_exploration         0.2914102146  0.29353889  0.33055456    1.000000000    0.32609569   0.89458659
ln_len_unique          0.2220856022  0.53772703  0.66331157    0.326095688    1.00000000   0.53400908
ln_added_sum           0.2664020253  0.36485491  0.44949580    0.894586586    0.53400908   1.00000000
ln_sim_best            1.0000000000  0.08055287  0.30757319    0.291410215    0.22208560   0.26640203
                        ln_sim_best
X                     -0.0815392574
Unnamed..0            -0.0815392574
phase                 -0.2475820684
novelty                0.0805528689
abs_perform_diff_best -0.3320894854
Q7_Q7_1               -0.0775427227
Q7_Q7_2               -0.0007748152
Q8_Q8_1               -0.0889023336
Q10                   -0.0087700658
count                  0.2511401493
total                  0.3075731914
user.requirement       0.2229356796
infovis                0.2888929166
novelty_score          0.1953208213
exploration            0.2914102146
Group                  0.0306239632
len_unique             0.2220856022
added_sum              0.2664020253
sim.to.best            1.0000000000
ln_novelty             0.0805528689
ln_total               0.3075731914
ln_exploration         0.2914102146
ln_len_unique          0.2220856022
ln_added_sum           0.2664020253
ln_sim_best            1.0000000000

library(car)

Loading required package: carData

mod <- lm(ln_total~ ln_novelty + ln_len_unique, data=df)
vif(mod)

   ln_novelty ln_len_unique 
      1.54079       1.54079 

df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_added_sum ~ factor(group), data=df)
summary(mod)

Call:
lm(formula = ln_added_sum ~ factor(group), data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0925 -1.7199 -0.4125  1.3091  6.7556 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)      2.0925     0.1588  13.175  < 2e-16 ***
factor(group)0  -0.6884     0.2231  -3.085  0.00213 ** 
factor(group)1  -0.3726     0.2204  -1.691  0.09133 .  
factor(group)2  -0.3643     0.2191  -1.663  0.09678 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.932 on 620 degrees of freedom
  (12 observations deleted due to missingness)
Multiple R-squared:  0.01515,   Adjusted R-squared:  0.01038 
F-statistic: 3.178 on 3 and 620 DF,  p-value: 0.02365

df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_novelty ~ factor(group), data=df)
summary(mod)

Call:
lm(formula = ln_novelty ~ factor(group), data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.52892 -0.14068  0.06865  0.15783  0.28954 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)     0.52892    0.01773  29.837  < 2e-16 ***
factor(group)0 -0.13269    0.02475  -5.362 1.16e-07 ***
factor(group)1 -0.12367    0.02445  -5.058 5.56e-07 ***
factor(group)2 -0.05178    0.02431  -2.130   0.0336 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2157 on 632 degrees of freedom
Multiple R-squared:  0.05844,   Adjusted R-squared:  0.05397 
F-statistic: 13.08 on 3 and 632 DF,  p-value: 2.706e-08

df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_total~ factor(group), data=df)
summary(mod)

Call:
lm(formula = ln_total ~ factor(group), data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.7373 -0.2143  0.3493  0.8471  1.7667 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)      5.1441     0.1181  43.541  < 2e-16 ***
factor(group)0  -1.0417     0.1649  -6.316 5.05e-10 ***
factor(group)1  -0.4069     0.1630  -2.497 0.012787 *  
factor(group)2  -0.5990     0.1620  -3.697 0.000237 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.437 on 632 degrees of freedom
Multiple R-squared:  0.06155,   Adjusted R-squared:  0.0571 
F-statistic: 13.82 on 3 and 632 DF,  p-value: 9.76e-09

df$group <- relevel(df$group, ref = "3")
mod_exp <- lm(ln_novelty ~ factor(group) + exploration, data=df)
summary(mod_exp)

Call:
lm(formula = ln_novelty ~ factor(group) + exploration, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.54106 -0.12727  0.05736  0.14886  0.31459 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)     0.47323    0.01879  25.186  < 2e-16 ***
factor(group)0 -0.11593    0.02395  -4.841 1.63e-06 ***
factor(group)1 -0.11252    0.02360  -4.768 2.31e-06 ***
factor(group)2 -0.03807    0.02349  -1.620    0.106    
exploration     0.18035    0.02541   7.098 3.42e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2077 on 631 degrees of freedom
Multiple R-squared:  0.1281,    Adjusted R-squared:  0.1225 
F-statistic: 23.17 on 4 and 631 DF,  p-value: < 2.2e-16

df$group <- relevel(df$group, ref = "3")
mod_exp_int <- lm(ln_novelty ~ factor(group) + exploration + factor(group) * exploration, data=df)
summary(mod_exp_int)

Call:
lm(formula = ln_novelty ~ factor(group) + exploration + factor(group) * 
    exploration, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.57237 -0.13115  0.05267  0.14288  0.33473 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                 0.51767    0.02277  22.731  < 2e-16 ***
factor(group)0             -0.18911    0.03024  -6.255 7.37e-10 ***
factor(group)1             -0.16723    0.03027  -5.524 4.86e-08 ***
factor(group)2             -0.07671    0.03033  -2.529 0.011673 *  
exploration                 0.03645    0.04944   0.737 0.461260    
factor(group)0:exploration  0.27701    0.07154   3.872 0.000119 ***
factor(group)1:exploration  0.18547    0.06885   2.694 0.007254 ** 
factor(group)2:exploration  0.11900    0.07214   1.650 0.099535 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2056 on 628 degrees of freedom
Multiple R-squared:  0.1498,    Adjusted R-squared:  0.1404 
F-statistic: 15.81 on 7 and 628 DF,  p-value: < 2.2e-16

anova(mod_exp_int, mod_exp)

Analysis of Variance Table

Model 1: ln_novelty ~ factor(group) + exploration + factor(group) * exploration
Model 2: ln_novelty ~ factor(group) + exploration
  Res.Df    RSS Df Sum of Sq      F   Pr(>F)   
1    628 26.541                                
2    631 27.221 -3  -0.67944 5.3588 0.001196 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

anova(mod, mod_exp)

Analysis of Variance Table

Model 1: ln_novelty ~ factor(group) + exploration + factor(group) * exploration
Model 2: ln_novelty ~ factor(group) + exploration
  Res.Df    RSS Df Sum of Sq      F   Pr(>F)   
1    628 26.541                                
2    631 27.221 -3  -0.67944 5.3588 0.001196 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_exploration ~ factor(group), data=df)
summary(mod)

Call:
lm(formula = ln_exploration ~ factor(group), data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.2373 -0.1828 -0.1553  0.1956  0.5269 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)     0.23727    0.01951  12.162  < 2e-16 ***
factor(group)0 -0.07103    0.02723  -2.608  0.00932 ** 
factor(group)1 -0.04822    0.02691  -1.792  0.07363 .  
factor(group)2 -0.05444    0.02676  -2.035  0.04231 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2373 on 632 degrees of freedom
Multiple R-squared:  0.01171,   Adjusted R-squared:  0.007015 
F-statistic: 2.495 on 3 and 632 DF,  p-value: 0.05892

df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_len_unique ~ factor(group), data=df)
summary(mod)

Call:
lm(formula = ln_len_unique ~ factor(group), data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.1090 -1.0190  0.1159  1.0643  5.0335 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)      4.1090     0.1570  26.176  < 2e-16 ***
factor(group)0  -1.1892     0.2205  -5.392 9.89e-08 ***
factor(group)1  -0.3145     0.2178  -1.444    0.149    
factor(group)2  -0.3315     0.2165  -1.531    0.126    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.91 on 620 degrees of freedom
  (12 observations deleted due to missingness)
Multiple R-squared:  0.04969,   Adjusted R-squared:  0.04509 
F-statistic: 10.81 on 3 and 620 DF,  p-value: 6.276e-07

tapply(df$ln_len_unique, df$group, summary)

$`3`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   3.135   4.007   4.109   4.691   8.514 

$`0`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   1.207   3.497   2.920   4.205   7.953       4 

$`1`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   2.303   3.961   3.794   4.997   8.415       4 

$`2`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   2.996   3.761   3.778   4.569   8.489       4 

df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_total ~ factor(group), data=df)
summary(mod)

Call:
lm(formula = ln_total ~ factor(group), data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.7373 -0.2143  0.3493  0.8471  1.7667 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)      5.1441     0.1181  43.541  < 2e-16 ***
factor(group)0  -1.0417     0.1649  -6.316 5.05e-10 ***
factor(group)1  -0.4069     0.1630  -2.497 0.012787 *  
factor(group)2  -0.5990     0.1620  -3.697 0.000237 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.437 on 632 degrees of freedom
Multiple R-squared:  0.06155,   Adjusted R-squared:  0.0571 
F-statistic: 13.82 on 3 and 632 DF,  p-value: 9.76e-09

df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_sim_best ~ factor(group), data=df)
summary(mod)

Call:
lm(formula = ln_sim_best ~ factor(group), data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.08356 -0.04310 -0.01492  0.02836  0.56217 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)     0.065334   0.005936  11.007  < 2e-16 ***
factor(group)0  0.018227   0.008338   2.186  0.02919 *  
factor(group)1 -0.015704   0.008231  -1.908  0.05689 .  
factor(group)2 -0.022506   0.008182  -2.751  0.00612 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.07123 on 604 degrees of freedom
  (28 observations deleted due to missingness)
Multiple R-squared:  0.04672,   Adjusted R-squared:  0.04199 
F-statistic: 9.868 on 3 and 604 DF,  p-value: 2.323e-06

df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_novelty ~ factor(group), data=df)
summary(mod)

Call:
lm(formula = ln_novelty ~ factor(group), data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.52892 -0.14068  0.06865  0.15783  0.28954 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)     0.52892    0.01773  29.837  < 2e-16 ***
factor(group)0 -0.13269    0.02475  -5.362 1.16e-07 ***
factor(group)1 -0.12367    0.02445  -5.058 5.56e-07 ***
factor(group)2 -0.05178    0.02431  -2.130   0.0336 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2157 on 632 degrees of freedom
Multiple R-squared:  0.05844,   Adjusted R-squared:  0.05397 
F-statistic: 13.08 on 3 and 632 DF,  p-value: 2.706e-08

df$group <- relevel(df$group, ref = "3")
mod2 <- lm(ln_exploration ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10, data=df)
summary(mod2)

Call:
lm(formula = ln_exploration ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + 
    Q8_Q8_1 + Q10, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.2702 -0.1858 -0.1430  0.1888  0.5506 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)     0.2923712  0.0404038   7.236 1.39e-12 ***
factor(group)0 -0.0698438  0.0278200  -2.511   0.0123 *  
factor(group)1 -0.0413484  0.0274994  -1.504   0.1332    
factor(group)2 -0.0569504  0.0271007  -2.101   0.0360 *  
Q7_Q7_1        -0.0042336  0.0080063  -0.529   0.5971    
Q7_Q7_2        -0.0002050  0.0081412  -0.025   0.9799    
Q8_Q8_1        -0.0110766  0.0084133  -1.317   0.1885    
Q10            -0.0002237  0.0122762  -0.018   0.9855    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2379 on 612 degrees of freedom
  (16 observations deleted due to missingness)
Multiple R-squared:  0.01539,   Adjusted R-squared:  0.004132 
F-statistic: 1.367 on 7 and 612 DF,  p-value: 0.2166

df$group <- relevel(df$group, ref = "3")
mod3 <- lm(ln_exploration ~  Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10, data=df)
summary(mod3)

Call:
lm(formula = ln_exploration ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.2419 -0.1927 -0.1442  0.1850  0.5191 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.2419076  0.0357812   6.761  3.2e-11 ***
Q7_Q7_1     -0.0038122  0.0079735  -0.478    0.633    
Q7_Q7_2     -0.0003276  0.0080966  -0.040    0.968    
Q8_Q8_1     -0.0091894  0.0084071  -1.093    0.275    
Q10         -0.0003380  0.0120660  -0.028    0.978    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2387 on 615 degrees of freedom
  (16 observations deleted due to missingness)
Multiple R-squared:  0.003783,  Adjusted R-squared:  -0.002696 
F-statistic: 0.5839 on 4 and 615 DF,  p-value: 0.6744

anova(mod2, mod3)

Analysis of Variance Table

Model 1: ln_exploration ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + 
    Q10 + count
Model 2: ln_exploration ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count
  Res.Df    RSS Df Sum of Sq      F Pr(>F)
1    611 29.907                           
2    614 30.200 -3  -0.29315 1.9964 0.1133

df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10, data=df)
summary(mod)

Call:
lm(formula = ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + 
    Q8_Q8_1 + Q10, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.51079 -0.10478  0.05577  0.15348  0.30800 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)     0.422670   0.035699  11.840  < 2e-16 ***
factor(group)0 -0.119068   0.024580  -4.844 1.61e-06 ***
factor(group)1 -0.116838   0.024297  -4.809 1.91e-06 ***
factor(group)2 -0.054815   0.023945  -2.289 0.022407 *  
Q7_Q7_1        -0.021129   0.007074  -2.987 0.002932 ** 
Q7_Q7_2         0.027944   0.007193   3.885 0.000114 ***
Q8_Q8_1         0.010681   0.007434   1.437 0.151262    
Q10             0.013412   0.010847   1.237 0.216739    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2102 on 612 degrees of freedom
  (16 observations deleted due to missingness)
Multiple R-squared:  0.09174,   Adjusted R-squared:  0.08136 
F-statistic: 8.831 on 7 and 612 DF,  p-value: 2.25e-10

df$group <- relevel(df$group, ref = "3")
mod1 <- lm(ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10, data=df)
summary(mod1)

Call:
lm(formula = ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + 
    Q8_Q8_1 + Q10, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.51079 -0.10478  0.05577  0.15348  0.30800 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)     0.422670   0.035699  11.840  < 2e-16 ***
factor(group)0 -0.119068   0.024580  -4.844 1.61e-06 ***
factor(group)1 -0.116838   0.024297  -4.809 1.91e-06 ***
factor(group)2 -0.054815   0.023945  -2.289 0.022407 *  
Q7_Q7_1        -0.021129   0.007074  -2.987 0.002932 ** 
Q7_Q7_2         0.027944   0.007193   3.885 0.000114 ***
Q8_Q8_1         0.010681   0.007434   1.437 0.151262    
Q10             0.013412   0.010847   1.237 0.216739    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2102 on 612 degrees of freedom
  (16 observations deleted due to missingness)
Multiple R-squared:  0.09174,   Adjusted R-squared:  0.08136 
F-statistic: 8.831 on 7 and 612 DF,  p-value: 2.25e-10

df$group <- relevel(df$group, ref = "3")
mod4 <- lm(ln_novelty ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod4)

Call:
lm(formula = ln_novelty ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + 
    count, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.7883 -0.0854  0.0699  0.1531  0.3014 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.343113   0.031746  10.808  < 2e-16 ***
Q7_Q7_1     -0.023135   0.007066  -3.274  0.00112 ** 
Q7_Q7_2      0.032111   0.007178   4.474 9.17e-06 ***
Q8_Q8_1      0.011171   0.007462   1.497  0.13490    
Q10         -0.001228   0.010785  -0.114  0.90939    
count        0.013646   0.002891   4.720 2.93e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2115 on 614 degrees of freedom
  (16 observations deleted due to missingness)
Multiple R-squared:  0.07716,   Adjusted R-squared:  0.06964 
F-statistic: 10.27 on 5 and 614 DF,  p-value: 1.82e-09

anova(mod1, mod4)

Analysis of Variance Table

Model 1: ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + 
    count
Model 2: ln_novelty ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count
  Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
1    611 26.099                                  
2    614 27.477 -3   -1.3777 10.751 6.815e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

library(lmerTest)

Loading required package: lme4
Loading required package: Matrix

Attaching package: ‘lmerTest’

The following object is masked from ‘package:lme4’:

    lmer

The following object is masked from ‘package:stats’:

    step

fit.lmer <- lmer(ln_novelty ~ factor(group) + ( 1 | phase), data = df, REML= FALSE)
fit.lmer

Linear mixed model fit by maximum likelihood  ['lmerModLmerTest']
Formula: ln_novelty ~ factor(group) + (1 | phase)
   Data: df
      AIC       BIC    logLik  deviance  df.resid 
-138.4479 -111.7167   75.2239 -150.4479       630 
Random effects:
 Groups   Name        Std.Dev.
 phase    (Intercept) 0.005242
 Residual             0.214918
Number of obs: 636, groups:  phase, 4
Fixed Effects:
   (Intercept)  factor(group)0  factor(group)1  factor(group)2  
       0.52892        -0.13269        -0.12367        -0.05178  

tapply(df$ln_novelty, df$group, summary)

$`3`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.4842  0.5588  0.5289  0.6162  0.6894 

$`0`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.5206  0.3962  0.6073  0.6858 

$`1`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.1777  0.5062  0.4053  0.6182  0.6931 

$`2`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.3871  0.5465  0.4771  0.6084  0.6904 

tapply(df$ln_total, df$group, summary)

$`3`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  4.331   4.761   5.079   5.144   5.515   5.891 

$`0`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   3.991   4.830   4.102   5.337   5.869 

$`1`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   4.553   5.089   4.737   5.580   5.882 

$`2`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   4.615   4.925   4.545   5.450   5.884 

tapply(df$ln_exploration, df$group, summary)

$`3`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.1379  0.2373  0.4612  0.6931 

$`0`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.1662  0.3393  0.6931 

$`1`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.00000 0.02545 0.18906 0.40035 0.69315 

$`2`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.00000 0.06417 0.18283 0.35241 0.69315 

tapply(df$ln_len_unique, df$group, summary)

$`3`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   3.135   4.007   4.109   4.691   8.514 

$`0`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   1.207   3.497   2.920   4.205   7.953       4 

$`1`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   2.303   3.961   3.794   4.997   8.415       4 

$`2`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   2.996   3.761   3.778   4.569   8.489       4 

tapply(df$ln_sim_best, df$group, summary)

$`3`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
0.00000 0.01062 0.05968 0.06533 0.10374 0.22040       4 

$`0`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
0.00000 0.00000 0.06578 0.08356 0.12579 0.41985       8 

$`1`
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
0.000000 0.002974 0.013236 0.049630 0.064522 0.611802        8 

$`2`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
0.00000 0.01304 0.03891 0.04283 0.06685 0.14108       8 

library(vtree)

Registered S3 methods overwritten by 'htmltools':
  method               from         
  print.html           tools:rstudio
  print.shiny.tag      tools:rstudio
  print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio
vtree version 5.6.5 -- For more information, type: vignette("vtree")

vtree(df, "group")

vtree(df, c("phase", "group"), 
   fillcolor = c( phase = "#e7d4e8", group = "#99d8c9"),
   horiz = FALSE)

df$group <- relevel(df$group, ref = "3")
mod5 <- lm(ln_total ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10, data=df)
summary(mod5)

Call:
lm(formula = ln_total ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + 
    Q10, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.9036 -0.2463  0.3243  0.8008  2.0358 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)     4.92970    0.23738  20.767  < 2e-16 ***
factor(group)0 -1.03251    0.16345  -6.317 5.13e-10 ***
factor(group)1 -0.42773    0.16157  -2.647 0.008320 ** 
factor(group)2 -0.63225    0.15922  -3.971 8.01e-05 ***
Q7_Q7_1        -0.20081    0.04704  -4.269 2.27e-05 ***
Q7_Q7_2         0.18707    0.04783   3.911 0.000102 ***
Q8_Q8_1        -0.08757    0.04943  -1.772 0.076957 .  
Q10             0.23953    0.07213   3.321 0.000950 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.398 on 612 degrees of freedom
  (16 observations deleted due to missingness)
Multiple R-squared:  0.1124,    Adjusted R-squared:  0.1022 
F-statistic: 11.07 on 7 and 612 DF,  p-value: 3.247e-13

df$group <- relevel(df$group, ref = "3")
mod6 <- lm(ln_total ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod6)

Call:
lm(formula = ln_total ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.5737 -0.1258  0.3665  0.7666  1.7353 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  4.19765    0.20821  20.160  < 2e-16 ***
Q7_Q7_1     -0.18970    0.04634  -4.093 4.82e-05 ***
Q7_Q7_2      0.19885    0.04708   4.224 2.77e-05 ***
Q8_Q8_1     -0.07884    0.04894  -1.611   0.1077    
Q10          0.17509    0.07073   2.475   0.0136 *  
count        0.13321    0.01896   7.025 5.71e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.387 on 614 degrees of freedom
  (16 observations deleted due to missingness)
Multiple R-squared:  0.1226,    Adjusted R-squared:  0.1154 
F-statistic: 17.16 on 5 and 614 DF,  p-value: 6.62e-16

anova(mod5, mod6)

Analysis of Variance Table

Model 1: ln_total ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + 
    count
Model 2: ln_total ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count
  Res.Df  RSS Df Sum of Sq      F    Pr(>F)    
1    611 1109                                  
2    614 1182 -3   -73.013 13.409 1.744e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

with(df, interaction.plot(group, phase, ln_total, ylim=c(0, max(ln_total)))) # interaction plot

with(df, interaction.plot(group, phase, ln_novelty, ylim=c(0, max(ln_novelty)))) # interaction plot

with(df, interaction.plot(group, phase, ln_exploration, ylim=c(0, max(ln_exploration)))) # interaction plot

with(df, interaction.plot(group, phase, ln_novelty, ylim=c(0, max(ln_novelty)))) # interaction plot